Considerations for Future-Proofing Digital First Nations Music Collections
Bibliographic record
Abstract
Researchers, when searching for musical material through an online catalogue, may scarcely consider how the resources included in the search results came to be suggested by the catalogue system. A catalogue’s search results are dependent on the metadata that describes the resources held within that collection, which can be stored in a variety of formats, standardised or unstandardised, each with their own benefits and limitations with respect to discoverability and interoperability with other systems. Music originating from First Nations communities around Australia often requires unique contextual knowledge in order to be discovered easily in a collection. Any First Nations music recordings that are held in a digital archive would ideally require that contextual knowledge to be stored as metadata, alongside the recording: this would ensure access conditions are respected (where appropriate), and information on which First Nations communities are involved is readily accessible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.133 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.034 | 0.071 |
| Open science | 0.011 | 0.025 |
| Research integrity | 0.022 | 0.014 |
| Insufficient payload (model declined to judge) | 0.074 | 0.020 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".